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Objective: The objective of this study was to analyze the current status of barriers to exercise participation (EP) among patients on maintenance hemodialysis (MHD). Materials and Methods: A cross-sectional study was conducted on 277 outpatients undergoing MHD in 2 tertiary first-class hospitals in Beijing from February 2023 to June 2023 who were selected using convenience sampling method. The data of patients on MHD were collected using the general information questionnaire, Physical Activity Rating Scale, Exercise Benefits/Barriers Scale (EBBS), and Exercise Self-Efficacy Scale (ESES). The relationship between EP and barriers to EP was analyzed through univariate and multivariate linear regression models. Results: Patients on MHD had a low exercise volume score of 13.71 ± 0.68 points and a medium EBBS score of 63.36 ± 0.40 points. Multivariate logistic analysis showed that exercise volume was significantly related to the following four aspects, including low monthly household income per capita (odds ratio [OR] = 86.741, 95% confidence interval [CI] [1.164–6.465], P = 0.042), primary underlying disease of diabetic nephropathy (OR = 45.993, 95% CI [1.353–1.564], P = 0.033), the belief that “fatigue in lower extremities hinders exercise” (OR = 4.697, 95% CI [1.127–19.585], P = 0.034), and the belief that “physical exercise bringing optimistic and positive life attitude” (OR = 0.074, 95% CI [0.007–0.830], P = 0.035). Conclusions: Since patients on MHD had low physical exercise volume, the health-care provider should pay more attention on the controllable factors that affect the EP of patients on MHD. Therefore, feasible and effective intervention measures can be formulated based on ESES in clinical nursing.

The possibility of fabricating metal nanoparticles using plant extracts has already been explored in phytomedicines. Nanobiotechnology has a wide range of potential applications, including the cosmetics industry, packaging, coatings, biomedicine, and the enhancement of biological activity. Nickel oxide (NiO) was fabricated in a waste-free, low-cost method and then used to adorn Marsdenia tenacissima extract (MTE), a crucial plant leaf. After forming the nanoparticles, MTE@NiO-NPs were also tested for their optical and biological qualities. The morphological characteristics of NiO-NPs are rhombohedra and slightly agglomerated, and the particle size of the biosynthesized NiO-NPs is observed in the range of ∼ 15 nm. The in vitro cytotoxicity of Marsdenia tenacissima extract (MTE) and nanoparticles on lung cancer cells (A549 and H1299) by MTT assay. The in vitro cytotoxicity assay results revealed that the newly fabricated nanoparticles effectively kill cancer cells with low minimal inhibitory concentration. The various biochemical staining assays assessed the cell death and morphological investigation. Furthermore, the mode of cell death was evaluated by mitochondrial membrane potential (Δψm) and reactive oxygen species (ROS). Therefore, the study concludes that the plant leaves extract of Marsdenia tenacissima is a powerful reducing/capping agent in fabricating MTE@NiO-NPs, demonstrating useful biological features.

BackgroundDue to the location of paragangliomas (PGLs) behind the retrohepatic segment of inferior vena cava (IVC), it is difficult to expose and resect the tumor.Case presentationA tumor measuring 50×45×62cm behind the retrohepatic portion of IVC was found in a 51-year-old female with hypertention and diabetes mellitus. Although the test for catecholamines revealed no signs of disease, the enhanced computed tomography (CT) scan, somatostatin receptor imaging and iodine-131-labeled metaiiodo-benzylguanidine (131I-MIBG) imaging revealed that the tumor was PGL. A three-dimensional printing was performed to visualize the tumor. The laparoscpic surgery for the PGL behind the retrohepatic segment of IVC was performed and the tumor was resected completely without causing any tissues injury. The pathologic diagnosis was PGL and the patient was able to recover well.ConclusionsThis case demonstrates that laparoscopic surgery may be helpful in tumor accessibility, and could be used in the appropriate cases to remove PGLs that are located behind the retrohepatic segment of the IVC.

Abstract Background There are some techniques for disassembly of hard nuclear. It is challenging in hard cataract surgery through microincision. The classic chop or prechop techniques often do not succeed,resulting in incomplete nuclear segmentation. The authors describe a new chop technique for removing hard nucleus cataracts in coaxial microincisional cataract surgery. Methods We create a deep hole (drill) in the central nucleus with the phaco tip and divide the nucleus (prechop) with the Nagahara chopper and the modified capsulorhexis forceps inside the hole. The chopper and the modified capsulorhexis forceps are spread apart laterally after they approach at the center of the nucleus, to create a complete fracture across the entire nucleus. Since January 2022, we have completed 27 eyes of 25 patients with hard nucleus cataract using this technique. Results Complete segmentation of the hard nuclear into two hemispheres was implemented with this drill and prechop technique in all cases. The effective phaco time and ultrasound energy decreased. No intraoperative complication such as iris injury, anterior capsule tears, zonulysis, or posterior capsule rupture with vitreous loss occurred during surgery. Conclusions This technique simplifies the previous prechop techniques especially for hard nucleus in microincisional cataract surgery. The technique is efficient, safe and simple.

Adrenocortical carcinoma (ACC) is a rare, heterogeneous, and aggressive malignancy with a generally poor prognosis. Surgical resection is the optimal treatment plan. After surgery, both mitotane treatment or the etoposide-doxorubicin-cisplatin (EDP) protocol plus mitotane chemotherapy have a certain effect, but there is still an extremely high possibility of recurrence and metastasis. The liver is one of the most common metastatic targets. Therefore, techniques such as transcatheter arterial chemoembolization (TACE) and microwave ablation (MWA) for liver tumors can be attempted in a specific group of patients. We present the case of a 44-year-old female patient with primary ACC, who was diagnosed with liver metastasis 6 years after resection. During mitotane treatment, we performed four courses of TACE and two MWA procedures in accordance with her clinical condition. The patient has maintained the partial response status and has currently returned to normal life to date. This case illustrates the value of the practical application of mitotane plus TACE and MWA treatment.

Abstract Tourniquet use always carries potential risks, which can range from mild transient functional impairments of thigh pain, skin blisters to severe permanent dysfunction of limb paralysis, nerve injuries or compartment syndrome. The ideal method for minimizing intraoperative tourniquet pressure (TP) for reducing postoperative complications remains controversial. In this prospective, randomized and controlled study, we reinvestigated an estimation formula for TP based on thigh circumferences and systolic blood pressure (SBP) with two traditional methods for TP determination in total knee arthroplasty (TKA): SBP plus 100 mmHg and a fixed value of 300 mmHg. TP values and postoperative thigh pain scores were compared among three groups. The intraoperative TP value of the formula-calculated group was lower than that of the traditional groups (14.7 mmHg, P = 0.3475 and 94.7 mmHg, P

Abstract To alleviate the social contradiction between limited medical resources and increasing medical needs, the medical image-assisted diagnosis based on deep learning has become the research focus in Wise Information Technology of med. Most of the existing medical segmentation models based on Convolution or Transformer have achieved relatively sound effects. However, the Convolution-based model with a limited receptive field cannot establish long-distance dependencies between features as the Network deepens. The Transformer-based model produces large computation overhead and cannot generalize the bias of local features and perceive the position feature of medical images, which are essential in medical image segmentation. To address those issues, we present Triple Gate MultiLayer Perceptron U-Net (TGMLP U-Net), a medical image segmentation model based on MLP, in which we design the Triple Gate MultiLayer Perceptron (TGMLP), composed of three parts. Firstly, considering encoding the position information of features, we propose the Triple MLP module based on MultiLayer Perceptron in this model. It uses linear projection to encode features from the high, wide, and channel dimensions, enabling the model to capture the long-distance dependence of features along the spatial dimension and the precise position information of features in three dimensions with less computational overhead. Then, we design the Local Priors and Global Perceptron module. The Global Perceptron divides the feature map into different partitions and conducts correlation modelling for each partition to establish the global dependency between partitions. The Local Priors uses multi-scale Convolution with high local feature extraction ability to explore further the relationship of context feature information within the structure. At last, we suggest a Gate-controlled Mechanism to effectively solves the problem that the dependence of position embeddings between Patches and within Patches in medical images cannot be well learned due to the relatively small number of samples in medical images segmentation data. Experimental results indicate that the proposed model outperforms other state-of-the-art models in most evaluation indicators, demonstrating its excellent performance in segmenting medical images.

Traditional target detection algorithms have difficulty to adapt complex environmental changes and have limited applicable scenarios. However, the deep-learning-based target detection model can automatically learn with strong generalization capability. In this article, we choose a single-stage deep-learning-based target detection model for research based on the model’s real-time processing requirements and to improve the accuracy and the robustness of target detection in remote sensing images. In addition, we improve the YOLOv4 network and present a new approach. First, we propose a classification setting of the nonmaximum suppression threshold to increase the accuracy without affecting the speed. Second, we study the anchor frame allocation problem in YOLOv4 and propose two allocation schemes. The proposed anchor frame scheme also improves the detection performance, and experimental results on the DOTA dataset validate their effectiveness.

Mitotane is the only drug approved to treat adrenocortical carcinoma (ACC), and a relationship of pharmacokinetic/pharmacodynamic has been characterized. However, limited evidence concerning affecting factors in large interindividual variability of the pharmacokinetics of mitotane is available. To address this question, a retrospective analysis was performed on ACC Chinese patients treated with mitotane for more than 3 months. Mitotane plasma trough concentrations were detected at the steady state, and CYP2B6, CYP3A4, and pregnane X receptor (PXR) polymorphisms were genotyped. After examining homogeneous pharmacologic data, we restricted the analyses to 36 patients that received mitotane for a median (interquartile range, IQR) of 9 months (5.00–22.50) with a median dose of 2 g/day (2.00–2.50). As a result, drug exposure was significantly influenced by the cumulative dose of mitotane, and CYP2B6 516GG and CYP2B6 26570CC were at high risk to be below the therapeutic range of mitotane. No association was found between mitotane concentrations with CYP3A4 or PXR polymorphism. Our data firstly indicated that the cumulative dose of mitotane and polymorphisms of CYP2B6 516 and CYP2B6 26570 might significantly affect mitotane plasma trough concentrations in Chinese ACC patients.

Abstract Prostate cancer (PCa) is a complex disease. An ongoing accumulation of mutations results in increased genetic diversity, with the tumor acquiring distinct subclones. However, non‐genetic intra‐tumoral heterogeneity, the cellular differentiation state and the interplay between subclonal evolution and transcriptional heterogeneity are poorly understood. Here, the authors perform single‐cell RNA sequencing from 14 untreated PCa patients. They create an extensive cell atlas of the PCa patients and mapped developmental states onto tumor subclonal evolution. They identify distinct subclones across PCa patients and then stratify tumor cells into four transcriptional subtypes, EMT‐like (subtype 0), luminal A‐like (subtype 1), luminal B/C‐like (subtype 2), and basal‐like (subtype 3). These subtypes are hierarchically organized into stem cell‐like and differentiated status. Strikingly, multiple subclones within a single primary tumor present with distinct combinations of preferential subtypes. In addition, subclones show different communication strengths with other cell types within the tumor ecosystem, which may modulate the distinct transcriptional subtypes of the subclones. Notably, by integrating TCGA data, they discover that both tumor cell transcriptional heterogeneity and cellular ecosystem diversity correlate with features of a poor prognosis. Collectively, their study provides the analysis of subclonal and transcriptional heterogeneity and its implication for patient prognosis.

Abstract Background To investigate the safety and efficacy of scleral concave pool trabeculectomy (SCPT) combined phacoemulsification for eyes with coexisting cataract and primary open-angle glaucoma (POAG). Methods This was a retrospective, controlled, interventional pilot case series. Thirty patients (30 eyes) were diagnosed with coexisting cataract and POAG between May 2015 and April 2018. Fourteen eyes underwent SCPT combined phacoemulsification were set as the study group, and 16 eyes received conventional phacotrabeculectomy were set as the control group. All patients were followed up for at least 6 months. The preoperative to postoperative changes in IOP, glaucoma medication requirements, BCVA, blebs functions, and adverse events were recorded. Results The groups were matched for baseline age, BCVA, IOP and types of IOP-lowering medications (all P > 0.05). At 6-month visit, there were no significant difference between control and study group in the improvement of BCVA (0.22 ± 0.24 versus 0.18 ± 0.26, P = 0.718), reduction of IOP (− 11.21 ± 8.61 mmHg versus − 9.19 ± 9.18 mmHg, P = 0.540) and the number of eyes that needed IOP-lowering medications (2 versus 3, P = 0.743). At the last visit, the rate of forming functioning blebs was significantly different between the study and control groups, (92.9% versus 68.7% respectively, P = 0.007). In the study group, 5 eyes developed hypotony, and 1 eye showed limited choroidal detachment, whereas in the control group 1 eye developed malignant glaucoma. All adverse events were successfully managed. Conclusion The SCPT combined phacoemulsification seems to be a safe and effective alternative to conventional phacotrabeculectomy for patients with POAG and visually significant cataract in the short-term.

In this work, we propose a robust tracking algorithm based on context-aware correlation filter framework. In order to improve the richness of the feature representation, the proposed hyper-feature which contains linearly weighted mixture of hand-crafted features (such as HOG, color histogram) and hierarchical deep convolutional features (such as VGGNet). The final output response map is optimized by the Gaussian constrained optimization method to control the response map follow the Gaussian distribution, which gain the robustness to target appearance variations. In addition, in terms of model update, an efficient adaptive model updating method is proposed to suppress the model noises significantly. Extensive experimental results on well-known tracking benchmark datasets to evaluate the proposed algorithm. Experimental results demonstrate that the proposed algorithm performs favorably against many state-of-the-art methods in terms of success rate, accuracy, and robustness.

Underwater images suffer from serious color distortion and detail loss because of the wavelength-dependent light absorption and scattering, which seriously influences the subsequent underwater object detection and recognition. The latest methods for underwater image enhancement are based on deep models, which focus on finding a mapping function from the underwater image subspace to a ground-truth image subspace. They neglect the diversity of underwater conditions which leads to different background colors of underwater images. In this paper, we propose a Class-condition Attention Generative Adversarial Network (CA-GAN) to enhance an underwater image. We build an underwater image dataset which contains ten categories generated by the simulator with different water attenuation coefficient and depth. Relying on the underwater image classes, CA-GAN creates a many-to-one mapping function for an underwater image. Moreover, in order to generate the realistic image, attention mechanism is utilized. In the channel attention block, the feature maps in the front-end layers and the back-end layers are fused along channels, and in the spatial attention block, feature maps are pixel-wise fused. Extensive experiments are conducted on synthetic and real underwater images. The experimental results demonstrate that CA-GAN can effectively recover color and detail of various scenes of underwater images and is superior to the state-of-the-art methods.

Ectopic Cushing’s syndrome due to ectopic ACTH&CRH-secreting by pheochromocytoma is extremely rare and can be fatal if not properly diagnosed. It remains unclear whether a unique cell type is responsible for multiple hormones secreting. In this work, we performed single-cell RNA sequencing to three different anatomic tumor tissues and one peritumoral tissue based on a rare case with ectopic ACTH&CRH-secreting pheochromocytoma. And in addition to that, three adrenal tumor specimens from common pheochromocytoma and adrenocortical adenomas were also involved in the comparison of tumor cellular heterogeneity. A total of 16 cell types in the tumor microenvironment were identified by unbiased cell clustering of single-cell transcriptomic profiles from all specimens. Notably, we identified a novel multi-functionally chromaffin-like cell type with high expression of both POMC (the precursor of ACTH) and CRH, called ACTH+&CRH + pheochromocyte. We hypothesized that the molecular mechanism of the rare case harbor Cushing’s syndrome is due to the identified novel tumor cell type, that is, the secretion of ACTH had a direct effect on the adrenal gland to produce cortisol, while the secretion of CRH can indirectly stimulate the secretion of ACTH from the anterior pituitary. Besides, a new potential marker (GAL) co-expressed with ACTH and CRH might be involved in the regulation of ACTH secretion. The immunohistochemistry results confirmed its multi-functionally chromaffin-like properties with positive staining for CRH, POMC, ACTH, GAL, TH, and CgA. Our findings also proved to some extent the heterogeneity of endothelial and immune microenvironment in different adrenal tumor subtypes.

The main cable and suspender of the spatial special-shaped suspension bridge are in the state of spatial stress; the structural stress is more complex than that of the parallel cable plane and finding the shape of the main cable of the spatial special-shaped cable plane is more difficult. In order to solve the problem of finding the shape of the main cable of the special-shaped suspension bridge, a new calculation model and algorithm of the main cable are proposed in this paper. The new calculation model adds constraints on the transverse bridge direction coordinates of the midspan on the basis of the original calculation model. The new calculation model can timely correct the calculation errors in all directions of the control node during the main cable shape finding process and improve the calculation accuracy. The new algorithm is a hybrid algorithm. The algorithm first uses the modified quantum genetic algorithm to solve and calculate the cable end force close to the real value, and then uses the cable end force obtained by the modified quantum genetic algorithm as the initial value to iterate through the modified least squares method. In this paper, a single cable plane curved suspension bridge is taken as the research background, and the differences of different calculation models in the shape-finding calculation of the main cable of the spatial special-shaped cable plane are compared. The results show that the proposed model is more stable in the calculation process, and the proposed algorithm has high accuracy and strong adaptability.

Sediment erosion is a negative phenomenon for the water turbine. The purpose of this paper is to study the effect of the guide vane opening on the particle motion and sediment erosion in the band chamber using the Euler–Lagrangian approach. The software Ansys CFX and Tabakoff erosion model are used to simulate the sediment laden flow in the full flow passage of the hydraulic turbine. The results are in good agreement with the actual erosion character on site. Results show that the guide vane opening has a positive correlation with the flow in the non-clearance channel. The increase of the guide vane opening will increase the erosion of the runner blade head, but the friction wear on the outlet side of the blade surface will decrease. The rotating action of the runner makes the sediment particles in the band chamber rotate rapidly around the center of the runner and constantly collide with the band chamber wall. Under the small opening, the smaller the opening, the easier the leakage of the band clearance occurs. The unsteady flow in the band chamber will disturb the motion trajectory of particles, change the impact angle of particles, and affect the wear in the band chamber. Under different openings, the change law of the erosion in the band clearance is closely related to the change law of clearance leakage.

Aconitine (AC) is the primary bioactive and secondary metabolite alkaloidin of Aconitum species which is accounted for more than 60% of the total diester-diterpenoid alkaloids in Aconite. To evaluate the analgesic effects of AC, 4 different pain models including hot plate assay, acetic acid writhing assay, formalin and CFA induced pain models were adopted in this study. In hot plate experiment, AC treatment at concentration of 0.3 mg/kg and 0.9 mg/kg improved the pain thresholds of mice similar to the positive drug aspirin at the concentration of 200 mg/kg (17.12% and 20.27% VS 19.21%). In acetic acid writhing experiment, AC significantly reduced the number of mice writhing events caused by acetic acid, and the inhibition rates were 68% and 76%. These results demonstrated that AC treatment revealed significant analgesic effects in both acute thermal stimulus pain model and chemically-induced visceral pain model. The biphasic nociceptive responses induced by formalin were significantly inhibited after AC treatment for 1h or 2h. The inhibition rates were 33.23% and 20.25% of AC treatment for 1h at 0.3 mg/kg and 0.9 mg/kg in phase I. In phase II, the inhibition rates of AC and aspirin were 36.08%, 32.48% and 48.82% respectively, which means AC showed similar analgesic effect to non-steroidal anti-inflammatory compounds. In the chronic CFA-induced nociception model, AC treatment also improved mice pain threshold to 131.33% at 0.3 mg/kg, which was similar to aspirin group (152.03%). Above all, our results verified that AC had obviously analgesic effects in different mice pain models.

Coronavirus disease has rapidly spread globally since early January of 2020. With millions of deaths, it is essential for an automated system to be utilized to aid in the clinical diagnosis and reduce time consumption for image analysis. This article presents a generative adversarial network (GAN)-based deep learning application for precisely regaining high-resolution (HR) CXR images from low-resolution (LR) CXR correspondents for COVID-19 identification. Respectively, using the building blocks of GAN, we introduce a modified enhanced super-resolution generative adversarial network plus (MESRGAN+) to implement a connected nonlinear mapping collected from noise-contaminated low-resolution input images to produce deblurred and denoised HR images. As opposed to the latest trends of network complexity and computational costs, we incorporate an enhanced VGG19 fine-tuned twin network with the wavelet pooling strategy in order to extract distinct features for COVID-19 identification. We demonstrate our proposed model on a publicly available dataset of 11,920 samples of chest X-ray images, with 2980 cases of COVID-19 CXR, healthy, viral and bacterial cases. Our proposed model performs efficiently both on the binary and four-class classification. The proposed method achieves accuracy of 98.8%, precision of 98.6%, sensitivity of 97.5%, specificity of 98.9%, an F1 score of 97.8% and ROC AUC of 98.8% for the multi-class task, while, for the binary class, the model achieves accuracy of 99.7%, precision of 98.9%, sensitivity of 98.7%, specificity of 99.3%, an F1 score of 98.2% and ROC AUC of 99.7%. Our method obtains state-of-the-art (SOTA) performance, according to the experimental results, which is helpful for COVID-19 screening. This new conceptual framework is proposed to play an influential role in addressing the issues facing COVID-19 examination and other diseases.

Computed Tomography has become a vital screening method for the detection of coronavirus 2019 (COVID-19). With the high mortality rate and overload for domain experts, radiologists, and clinicians, there is a need for the application of a computerized diagnostic technique. To this effect, we have taken into consideration improving the performance of COVID-19 identification by tackling the issue of low quality and resolution of computed tomography images by introducing our method. We have reported about a technique named the modified enhanced super resolution generative adversarial network for a better high resolution of computed tomography images. Furthermore, in contrast to the fashion of increasing network depth and complexity to beef up imaging performance, we incorporated a Siamese capsule network that extracts distinct features for COVID-19 identification.The qualitative and quantitative results establish that the proposed model is effective, accurate, and robust for COVID-19 screening. We demonstrate the proposed model for COVID-19 identification on a publicly available dataset COVID-CT, which contains 349 COVID-19 and 463 non-COVID-19 computed tomography images. The proposed method achieves an accuracy of 97.92%, sensitivity of 98.85%, specificity of 97.21%, AUC of 98.03%, precision of 98.44%, and F1 score of 97.52%. Our approach obtained state-of-the-art performance, according to experimental results, which is helpful for COVID-19 screening. This new conceptual framework is proposed to play an influential task in the issue facing COVID-19 and related ailments, with the availability of few datasets.

It is a well-known fact that diabetic retinopathy (DR) is one of the most common causes of visual impairment between the ages of 25 and 74 around the globe. Diabetes is caused by persistently high blood glucose levels, which leads to blood vessel aggravations and vision loss. Early diagnosis can minimise the risk of proliferated diabetic retinopathy, which is the advanced level of this disease, and having higher risk of severe impairment. Therefore, it becomes important to classify DR stages. To this effect, this paper presents a weighted fusion deep learning network (WFDLN) to automatically extract features and classify DR stages from fundus scans. The proposed framework aims to treat the issue of low quality and identify retinopathy symptoms in fundus images. Two channels of fundus images, namely, the contrast-limited adaptive histogram equalization (CLAHE) fundus images and the contrast-enhanced canny edge detection (CECED) fundus images are processed by WFDLN. Fundus-related features of CLAHE images are extracted by fine-tuned Inception V3, whereas the features of CECED fundus images are extracted using fine-tuned VGG-16. Both channels’ outputs are merged in a weighted approach, and softmax classification is used to determine the final recognition result. Experimental results show that the proposed network can identify the DR stages with high accuracy. The proposed method tested on the Messidor dataset reports an accuracy level of 98.5%, sensitivity of 98.9%, and specificity of 98.0%, whereas on the Kaggle dataset, the proposed model reports an accuracy level of 98.0%, sensitivity of 98.7%, and specificity of 97.8%. Compared with other models, our proposed network achieves comparable performance.

Pneumonia is a prevalent severe respiratory infection that affects the distal and alveoli airways. Across the globe, it is a serious public health issue that has caused high mortality rate of children below five years old and the aged citizens who must have had previous chronic-related ailment. Pneumonia can be caused by a wide range of microorganisms, including virus, fungus, bacteria, which varies greatly across the globe. The spread of the ailment has gained computer-aided diagnosis (CAD) attention. This paper presents a multi-channel-based image processing scheme to automatically extract features and identify pneumonia from chest X-ray images. The proposed approach intends to address the problem of low quality and identify pneumonia in CXR images. Three channels of CXR images, namely, the Local Binary Pattern (LBP), Contrast Enhanced Canny Edge Detection (CECED), and Contrast Limited Adaptive Histogram Equalization (CLAHE) CXR images are processed by deep neural networks. CXR-related features of LBP images are extracted using shallow CNN, features of the CLAHE CXR images are extracted by pre-trained inception-V3, whereas the features of CECED CXR images are extracted using pre-trained MobileNet-V3. The final feature weights of the three channels are concatenated and softmax classification is utilized to determine the final identification result. The proposed network can accurately classify pneumonia according to the experimental result. The proposed method tested on publicly available dataset reports accuracy of 98.3%, sensitivity of 98.9%, and specificity of 99.2%. Compared with the single models and the state-of-the-art models, our proposed network achieves comparable performance.

Abstract The high energy density of room temperature (RT) sodium–sulfur batteries (Na‐S) usually rely on the efficient conversion of polysulfide to sodium sulfide during discharging and sulfur recovery during charging, which is the rate‐determining step in the electrochemical reaction process of Na‐S batteries. In this work, a 3D network (Ni‐NCFs) host composed by nitrogen‐doped carbon fibers (NCFs) and Ni hollow spheres is synthesized by electrospinning. In this novel design, each Ni hollow unit not only can buffer the volume fluctuation of S during cycling, but also can improve the conductivity of the cathode along the carbon fibers. Meanwhile, the result reveals that a small amount of Ni is polarized during the sulfur‐loading process forming a polar NiS bond. Furthermore, combining with the nitrogen‐doped carbon fibers, the Ni‐NCFs composite can effectively adsorb soluble polysulfide intermediate, which further facilitates the catalysis of the Ni unit for the redox of sodium polysulfide. In addition, the in situ Raman is employed to supervise the variation of polysulfide during the charging and discharging process. As expected, the freestanding S@Ni‐NCFs cathode exhibits outstanding rate capability and excellent cycle performance.

Vehicle Re-identification (re-id) over surveillance camera network with non-overlapping field of view is an exciting and challenging task in intelligent transportation systems (ITS). Due to its versatile applicability in metropolitan cities, it gained significant attention. Vehicle re-id matches targeted vehicle over non-overlapping views in multiple camera network. However, it becomes more difficult due to inter-class similarity, intra-class variability, viewpoint changes, and spatio-temporal uncertainty. In order to draw a detailed picture of vehicle re-id research, this paper gives a comprehensive description of the various vehicle re-id technologies, applicability, datasets, and a brief comparison of different methodologies. Our paper specifically focuses on vision-based vehicle re-id approaches, including vehicle appearance, license plate, and spatio-temporal characteristics. In addition, we explore the main challenges as well as a variety of applications in different domains. Lastly, a detailed comparison of current state-of-the-art methods performances over VeRi-776 and VehicleID datasets is summarized with future directions. We aim to facilitate future research by reviewing the work being done on vehicle re-id till to date.

Discriminative correlation filter (DCF) based tracking algorithms have obtained prominent speed and accuracy strengths, which have attracted extensive attention and research. However, some unavoidable deficiencies still exist. For example, the circulant shifted sampling process is likely to cause repeated periodic assumptions and cause boundary effects, which degrades the tracker’s discriminative performance, and the target is not easy to locate in complex appearance changes. In this paper, a spatial–temporal regularization module based on BACF (background-aware correlation filter) framework is proposed, which is performed by introducing a temporal regularization to deal effectively with the boundary effects issue. At the same time, the accuracy of target recognition is improved. This model can be effectively optimized by employing the alternating direction multiplier (ADMM) method, and each sub-problem has a corresponding closed solution. In addition, in terms of feature representation, we combine traditional hand-crafted features with deep convolution features linearly enhance the discriminative performance of the filter. Considerable experiments on multiple well-known benchmarks show the proposed algorithm is performs favorably against many state-of-the-art trackers and achieves an AUC score of 64.4% on OTB-100.

Inflammatory bowel disease (IBD) is a chronic immune-related disease mainly caused by the disequilibrium of T helper (Th) cell paradigm? Pogostone (PO) is one of the major chemical constituents of Pogostemon cablin (Blanco) Benth. The present study aims to investigate the potential benefit of PO against IBD in a 2,4,6-trinitrobenzenesulfonic acid (TNBS)-induced experimental colitis model. PO treatment by enema significantly brought down the disease activity index (DAI) of the TNBS-challenged rats, which was manifested by the ameliorated inflammatory features including ulceration, adhesion, and edema. Hematoxylin-eosin (HE) staining and immunohistochemistry analysis showed that PO effectively relived colon damage by restoring epithelium, and more importantly, by inhibiting the infiltration of pro-inflammatory Th1 and Th17 cells in the colon. Additionally, PO inhibited the activity of myeloperoxidase and secretion of inflammatory cytokines including IFN-γ, IL-12p70, IL-17A, and IL-10. Together with our previous findings, the present data indicated that the anti-IBD effect of PO probably related to its direct inhibition on Th cell proliferation and suppression of the cytokines secretion. These results highlighted the potential of PO as a promising candidate to relieve IBD.

The intelligent transportation system is currently an active research area, and vehicle re-identification (Re-Id) is a fundamental task to implement it. It determines whether the given vehicle image obtained from one camera has already appeared over a camera network or not. There are many possible practical applications where the vehicle Re-Id system can be employed, such as intelligent vehicle parking, suspicious vehicle tracking, vehicle incident detection, vehicle counting, and automatic toll collection. This task becomes more challenging because of intra-class similarity, viewpoint changes, and inconsistent environmental conditions. In this paper, we propose a novel approach that re-identifies a vehicle in two steps: first we shortlist the vehicle from a gallery set on the basis of appearance, and then in the second step we verify the shortlisted vehicle’s license plates with a query image to identify the targeted vehicle. In our model, the global channel extracts the feature vector from the whole vehicle image, and the local region channel extracts more discriminative and salient features from different regions. In addition to this, we jointly incorporate attributes like model, type, and color, etc. Lastly, we use a siamese neural network to verify license plates to reach the exact vehicle. Extensive experimental results on the benchmark dataset VeRi-776 demonstrate the effectiveness of the proposed model as compared to various state-of-the-art methods.

To evaluate the clinical efficacy of sorafenib as preoperative neoadjuvant therapy in patients with high risk renal cell carcinoma (RCC).Clinical data of 18 patients with high risk RCC who received surgery done successfully after preoperative neoadjuvant therapy with sorafenib in Peking Union Medical College Hospital (PUMCH) from April 2007 to October 2013 have been reviewed and analyzed in this study.Among the 18 patients there were 13 male and 5 female, with a median age of 54.6 years. The objective response rate (ORR) of the operation on the selected patients is very high (94.4%), including 4 cases (22.2%) of partial response (PR) and 13 cases (72.2%) of stable disease (SD). After preoperative sorafenib treatment, the average tumor size of the 18 patients decreased from 7.8 cm (ranging from 3.6 to 19.2 cm) to 6.2 cm (ranging from 2.4 to 16.8 cm), and the median value of average tumor CT value decreased from 61HU to 52 HU. Among the 5 patients who had IVC tumor thrombi, the grades of tumor thrombi in 2 patients who were grade II before sorafenib treatment became grade I and grade 0 respectively, 2 patients of grade III both became grade II.Preoperative neoadjuvant therapy with sorafenib for high risk RCC patients can significantly decrease primary tumor volume as well as tumor thrombus, which could help the nephron-sparing surgery (NSS) or radical nephrectomy to be done successfully.

By virtue of fractional integral identities, incomplete beta function, useful series, and inequalities, we apply the concept of GG-convex function to derive new type Hermite-Hadamard inequalities involving Hadamard fractional integrals. Finally, some applications to special means of real numbers are demonstrated.